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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Bulletin of Computer Science Research Aksi Kita: Jurnal Pengabdian Kepada Masyarakat
Rossy Nurhasanah
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences Other

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Penerapan Probabilistic Neural Network pada Klasifikasi Patogen Daun Bibit Jabon Berdasarkan Ciri Morfologi Spora Melly Br Bangun; Yeni Herdiyeni; Elis Nina Herliyana; Rossy Nurhasanah
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.325

Abstract

The aim of this research is to clasify pathogen of Jabon’s leaf seedling based on spora morphological features using Probabilistic Neural Network classifier. Three types of pathogen to be classified are Colletotrichum sp., Curvularia sp., and Fusarium sp.. The methodologies used are data acquisition using optilab camera microscope to obtain microscopic image data , preprocessing (grayscale, median smoothing, thresholding Otsu, region filling, median smoothing and dilate), morphology feature extraction (area, perimeter, area convex, convex perimeter, compactness, solidity, convexity and roundness), Probabilistic Neural Network classification, and evaluation. The basic morphological characteristics consisting of area, perimeter, convex area, convex perimeter, and derived morphological characteristics consisting of compactness, solidity, convexity and roundness. The experimental results of the morphological feature extraction showed that the compactness and roundness characteristics can be used to identify the three types of pathogens because with these characteristics each class of pathogen is separate. Testing for this research was carried out using 150 test data from three classes of objects from the dataset, namely class 1 (Colletotrichum sp.), class 2 (Curvularia sp.), and class 3 (Fusarium sp.). Then the results of pathogen classification using the application of the PNN algorithm in testing this research obtained an average accuracy value of 86.8% with a proportion of training data and test data of 80:20. The results of the PNN classification on 150 test data were that there were 36 data classified into Colletotrichum sp., 44 data classified into Curvularia sp., and 50 data classified into Fusarium sp. Further research could be done with the identification of digital microscopic images without cropping and systems that could clasify a colony image of pathogens clearly.
Topic Modelling on Beauty Product Reviews Using Latent Dirichlet Allocation Ade Sarah Huzaifah; Rossy Nurhasanah; R. A. Fattah Adriansyah
Jurnal Ilmu Komputer dan Agri-Informatika Vol 12 No 1 (2025)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.1.119-131

Abstract

Today, beauty products are essential especially for women. Because they're so popular, review sites now offer information on products. These internet reviews include views on trends, customer satisfaction and product performance. It's hard to draw conclusions from so many reviews. Large amounts of text have been analyzed using topic modelling techniques like Latent Dirichlet Allocation (LDA). LDA is a probabilistic model that explains data and why some sections are similar. LDA is a useful tool for text mining and information retrieval. Recent studies show that topic modelling for product reviews in the cosmetics business can provide useful insights into consumer perceptions and product qualities. The purpose of this study is to examine themes in customer evaluations of ten different brands of face wash products from the female daily website. Data collection, preprocessing, topic modeling using LDA, visualization, and interpretation of topics are all steps in the research procedure. The findings show that Topic 2, which captures user conversations about product benefits that users prefer, is the most frequently discussed topic by users when evaluating a product (48.5%). This is followed by Topic 1, which captures user conversations about the effects of products on acne-prone skin (38%). Finally, Topic 3 captures user conversations about products based on natural ingredients (13.5%). These topics provide valuable insights for both manufacturers, who can improve product offerings, and consumers, helping them make informed purchasing decisions.
Enhancing Understanding of AI-Based Digital Business Through Interactive Seminars for Information Technology Students Ade Sarah Huzaifah; Rossy Nurhasanah; Fanindia Purnamasari; Dedy Arisandi; Ivan Jaya
Aksi Kita: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 4 (2025): JULI-AGUSTUS
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/vk045k91

Abstract

The development of artificial intelligence (AI) technology has become a major driver in the transformation of the digital business world, including in the startup sector. However, a deep understanding of AI integration into business models remains a challenge for students, particularly in the field of Information Technology (IT). This community service activity aims to enhance the knowledge and skills of IT students in designing strategic, ethical, and sustainable AI-based digital businesses. The implementation method involves a one-day educational seminar, including presentations, interactive discussions, simulations of Business Model Canvas (BMC) development, and evaluation through questionnaires. Evaluation results showed significant improvements: understanding of the BMC increased from 41% to 89%, understanding of AI startup concepts from 54% to 92%, ability to draft a business plan from 16% to 78%, and motivation for technology entrepreneurship from 68% to 90%. These findings indicate that an applied and participatory approach in seminars is effective in developing digital entrepreneurship capacity among IT students.
Co-Authors Ade Sarah Huzaifah Dedy Arisandi Elis Nina Herliyana Fanindia Purnamasari Ivan Jaya Melly Br Bangun R. A. Fattah Adriansyah